In many image processing applications, such as hyperspectral image applications, several image sensors are employed to obtain images of a region of interest. Such images usually present abundant multidimensional information which contains several image bands. Typically, these images are fused together to form a single image. The single fused image contains features extracted from all the images that were originally received.
There are several image fusion techniques that can be applied to combine multiple images with varying information into one fused image. Specifically, in hyperspectral image applications, where several hundreds of images may be required to be fused into one image, image fusion techniques of multiband image fusion are employed. In the multiband image fusion technique, several images of the same spatial resolution are combined to form a single image.
Several challenges exist while fusing multiple images into a single image Computational costs of processing can be high because of the large number of image bands present in the image set. In addition, storing the images requires larger memory as a single hyperspectral image dataset may contain a large number of image bands. Specifically, in hyperspectral applications, nearby image bands in the hyperspectral data cube exhibit a high degree of spatial correlation amongst them due to the contiguous and narrow nature of the sensors. Redundant data need to be removed for the efficient processing and reduction in the computational overheads.